Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding
space and then matching them by nearest neighbor search. This requires strong locality …

A common thread of retrieval-augmented methods in the existing literature focuses on
retrieving encyclopedic knowledge, such as Wikipedia, which facilitates well-defined entity …

Passage retrieval is a fundamental task in information retrieval (IR) research, which has
drawn much attention recently. In the English field, the availability of large-scale annotated …

Deep pre-trained language models (e, g. BERT) are effective at large-scale text retrieval
task. Existing text retrieval systems with state-of-the-art performance usually adopt a retrieve …

In this paper, we consider the problem of improving the inference latency of language model-
based dense retrieval systems by introducing structural compression and model size …

The advent of pre-trained language models (PLMs) has indisputably revolutionized the field
of natural language processing (NLP). Prior to their emergence, NLP research …

Large language models effectively generate contextualized word representations across
languages, domains, and tasks. Drive by these abilities, these models have become a build …

In the past years neural retrieval approaches using contextualized language models have
driven advancements in information retrieval (IR) and demonstrated great effectiveness …